Resource aware placement of IoT application modules in Fog-Cloud Computing Paradigm

Taneja, Mohit; Davy, Alan

doi:10.23919/inm.2017.7987464

Cited by 263 publications

(216 citation statements)

References 19 publications

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“…In a few cases, the energy was not the optimization objective, but it was analyzed to validate the benefits of the proposals. For example, Taneja and Davy mainly addressed the minimization of the application execution time, but they also analyzed other common metrics such as network usage and energy consumption.…”

Section: Analysis Of the State Of The Artmentioning

confidence: 99%

How to place your apps in the fog: State of the art and open challenges

et al. 2019

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Summary Fog computing aims at extending the cloud towards the Internet of things so to achieve improved quality of service and to empower latency‐sensitive and bandwidth‐hungry applications. The fog calls for novel models and algorithms to distribute multiservice applications in such a way that data processing occurs wherever it is best placed, based on both functional and nonfunctional requirements. This survey reviews the existing methodologies to solve the application placement problem in the fog, while pursuing three main objectives. First, it offers a comprehensive overview on the currently employed algorithms, on the availability of open‐source prototypes and on the size of test use cases. Second, it classifies the literature based on the application and fog infrastructure characteristics that are captured by available models, with a focus on the considered constraints and the optimized metrics. Finally, it identifies some open challenges in application placement in the fog.

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Section: Analysis Of the State Of The Artmentioning

confidence: 99%

How to place your apps in the fog: State of the art and open challenges

et al. 2019

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“…The suitability of fog computing in context of IoT has been studied by Sarkar et al The approach not only optimizes the use of available in‐network compute and storage resources but also reduces the bandwidth and storage requirements to upload data on cloud . Chiang and Zhang survey main research challenges involved in fog and network context of IoT and how fog can help address them.…”

Section: Literature Review Background and Motivationmentioning

confidence: 99%

“…The designed and built microservices of SmartHerd IoT ecosystem are placed across network edge at SmartHerd IoT Gateway and Cloud, ie, IBM Cloud in a manner that latency‐sensitive one and the ones with immediate data requirement are placed at the fog node and the remaining ones are placed at the cloud with the desired resource requirement of each of the service being met in an efficient manner.…”

Section: Smartherd Management Iot Platformmentioning

confidence: 99%

SmartHerd management: A microservices‐based fog computing–assisted IoT platform towards data‐driven smart dairy farming

et al. 2019

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Summary Internet of Things (IoT), fog computing, cloud computing, and data‐driven techniques together offer a great opportunity for verticals such as dairy industry to increase productivity by getting actionable insights to improve farming practices, thereby increasing efficiency and yield. In this paper, we present SmartHerd, a fog computing–assisted end‐to‐end IoT platform for animal behavior analysis and health monitoring in a dairy farming scenario. The platform follows a microservices‐oriented design to assist the distributed computing paradigm and addresses the major issue of constrained Internet connectivity in remote farm locations. We present the implementation of the designed software system in a 6‐month mature real‐world deployment, wherein the data from wearables on cows is sent to a fog‐based platform for data classification and analysis, which includes decision‐making capabilities and provides actionable insights to farmer towards the welfare of animals. With fog‐based computational assistance in the SmartHerd setup, we see an 84% reduction in amount of data transferred to the cloud as compared with the conventional cloud‐based approach.

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“…Moreover, one of the main challenges that still remains is how to provide proper resource allocation mechanisms for Smart City applications, since services can be placed in a highly congested location, which would result in a higher communication latency [4]. In the literature, few resource provisioning strategies are currently addressing the stringent requirements of Smart City applications while minimizing resource costs and maximizing Quality of Service (QoS) [4], [5]. Therefore, efficient resource allocation strategies are needed to address these issues.…”

Section: Introductionmentioning

confidence: 99%

City of Things: Enabling Resource Provisioning in Smart Cities

et al. 2018

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Abstract-In the last years, traffic over wireless networks has been increasing exponentially due to the impact of Internet of Things (IoT). IoT is transforming a wide range of services in different domains of urban life, such as environmental monitoring, home automation and public transportation. The so-called Smart City applications will introduce a set of stringent requirements, such as low latency and high mobility, since services must be allocated and instantiated on-demand simultaneously close to multiple devices at different locations. Efficient resource provisioning functionalities are needed to address these demanding constraints introduced by Smart City applications while minimizing resource costs and maximizing Quality of Service (QoS). In this article, the City of Things (CoT) framework is presented, which provides not only data collection and analysis functionalities but also automated resource provisioning mechanisms for future Smart City applications. CoT is deployed as a Smart City testbed in Antwerp (Belgium) that allows researchers and developers to easily setup and validate IoT experiments. A Smart City use case based on Air Quality Monitoring through the deployment of air quality sensors in moving cars has been presented showing the full applicability of the CoT framework for a flexible and scalable resource provisioning in the Smart City ecosystem.

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Resource aware placement of IoT application modules in Fog-Cloud Computing Paradigm

Cited by 263 publications

References 19 publications

How to place your apps in the fog: State of the art and open challenges

How to place your apps in the fog: State of the art and open challenges

SmartHerd management: A microservices‐based fog computing–assisted IoT platform towards data‐driven smart dairy farming

City of Things: Enabling Resource Provisioning in Smart Cities

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